Rbf neural network - Open Access .click

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Efficient training of RBF neural networks for pattern recognition [PDF]

IEEE Transactions on Neural Networks, 2001
The problem of training a radial basis function (RBF) neural network for distinguishing two disjoint sets in R(n) is considered. The network parameters can be determined by minimizing an error function that measures the degree of success in the recognition of a given number of training patterns.
F. LAMPARIELLO, SCIANDRONE, MARCO
openaire +5 more sources

Large‐Scale Machine Learning to Screen for Small‐Molecule Senolytics

Advanced Intelligent Discovery, EarlyView.
A consistent workflow underpins all experiments in this study. A dedicated model‐selection dataset first identifies optimal hyperparameters for each algorithm. Models are then trained and rigorously evaluated on independent sets of molecules using the senolytic ratio SR. Comprehensive hyperparameter exploration across SMILES representations, task types,
Alexis Dougha, Jesus Gil, Pedro J. Ballester +2 more
wiley +1 more source

A fault line selection method for small current grounding system

Gong-kuang zidonghua, 2013
In view of problem that fault line selection method for small current grounding system is difficult to be suitable for different grounding modes, the paper proposed a fault line selection method for small current grounding system based on RBF neural ...
SHI Dan, SHAO Ru-ping, XU Ju
doaj +1 more source

Accelerating Discovery of Organic Molecular Crystals via Materials Informatics and Autonomous Experiments

Advanced Intelligent Discovery, EarlyView.
Materials informatics and autonomous experimentation are transforming the discovery of organic molecular crystals. This review presents an integrated molecule–crystal–function–optimization workflow combining machine learning, crystal structure prediction, and Bayesian optimization with robotic platforms.
Takuya Taniguchi, Kazuki Ishizaki, Ryo Fukasawa +2 more
wiley +1 more source

Application of a radial basis function neural network for diagnosis of diabetes mellitus [PDF]

, 2006
In this article an attempt is made to study the applicability of a general purpose, supervised feed forward neural network with one hidden layer, namely. Radial Basis Function (RBF) neural network.
Anitha, S, Venkatesan, P
core

Bayesian Optimisation for the Experimental Sciences: A Practical Guide to Data‐Efficient Optimisation of Laboratory Workflows

Advanced Intelligent Systems, EarlyView.
This study provides an introduction to Bayesian optimisation targeted for experimentalists. It explains core concepts, surrogate modelling, and acquisition strategies, and addresses common real‐world challenges such as noise, constraints, mixed variables, scalability, and automation.
Chuan He, Martin Singull, T. Jesper Jacobsson +2 more
wiley +1 more source

Retinal Vessel Segmentation: A Comprehensive Review From Classical Methods to Deep Learning Advances (1982–2025)

Advanced Intelligent Systems, EarlyView.
Four decades of retinal vessel segmentation research (1982–2025) are synthesized, spanning classical image processing, machine learning, and deep learning paradigms. A meta‐analysis of 428 studies establishes a unified taxonomy and highlights performance trends, generalization capabilities, and clinical relevance.
Avinash Bansal +6 more
wiley +1 more source

Optimization of modeling and temperature control of air-cooled PEMFC based on TLBO-DE

Energy and AI
The temperature control of the air-cooled proton exchange membrane fuel cell (PEMFC) is important for effective and safe operation. To develop a practical and precise controller, this study combines the Radial Basis Function (RBF) neural network with ...
Pu He +9 more
doaj +1 more source

Optimizing Structural Parameters of a Water Distribution Device in Cylindrical High‐Flowrate Mixed Bed Using Multiobjective Genetic Algorithm

Asia-Pacific Journal of Chemical Engineering, EarlyView.
ABSTRACT High‐flowrate mixed bed is the foremost desalination equipment in the condensate polishing system. The water distribution device determining the water distribution uniformity directly affects its operation stability, output water quality, and service life of the resins.
Jing Zhu +5 more
wiley +1 more source

Implementation of Machine Learning Models to Predict Functionality of Pea Flour From Its Composition

Cereal Chemistry, EarlyView.
ABSTRACT Background and Objectives The goal of this research was to examine the relationship between the composition and functionality of pea flour using the following machine learning algorithms: linear regression, partial least squares regression (PLSR), Gaussian process regression (GPR), support vector regression, gradient‐boosted decision trees ...
Colten N. Nickerson +7 more
wiley +1 more source

mechanical engineering and machinery